class_docstr: The DDPOTrainer uses Deep Diffusion Policy Optimization to optimise diffusion models.
  Note, this trainer is heavily inspired by the work here: https://github.com/kvablack/ddpo-pytorch
  As of now only Stable Diffusion based pipelines are supported
  
  Attributes:
      **config** (`DDPOConfig`) -- Configuration object for DDPOTrainer. Check the documentation of `PPOConfig` for more
       details.
      **reward_function** (Callable[[torch.Tensor, Tuple[str], Tuple[Any]], torch.Tensor]) -- Reward function to be used
      **prompt_function** (Callable[[], Tuple[str, Any]]) -- Function to generate prompts to guide model
      **sd_pipeline** (`DDPOStableDiffusionPipeline`) -- Stable Diffusion pipeline to be used for training.
      **image_samples_hook** (Optional[Callable[[Any, Any, Any], Any]]) -- Hook to be called to log images
docstr: Generate samples from the model
  
  Args:
      iterations (int): Number of iterations to generate samples for
      batch_size (int): Batch size to use for sampling
  
  Returns:
      samples (List[Dict[str, torch.Tensor]]), prompt_image_pairs (List[List[Any]])
docstr_tok: ["Generate","samples","from","the","model","Args",":","iterations","(","int",")",":","Number","of","iterations","to","generate","samples","for","batch_size","(","int",")",":","Batch","size","to","use","for","sampling","Returns",":","samples","(","List","[","Dict","[","str",",","torch.Tensor","]","]",")",",","prompt_image_pairs","(","List","[","List","[","Any","]","]",")"]
code: def _generate_samples(self, iterations, batch_size):
      """
      Generate samples from the model
  
      Args:
          iterations (int): Number of iterations to generate samples for
          batch_size (int): Batch size to use for sampling
  
      Returns:
          samples (List[Dict[str, torch.Tensor]]), prompt_image_pairs (List[List[Any]])
      """
      samples = []
      prompt_image_pairs = []
      self.sd_pipeline.unet.eval()
  
      sample_neg_prompt_embeds = self.neg_prompt_embed.repeat(
          batch_size, 1, 1
      )
  
      for _ in range(iterations):
          prompts, prompt_metadata = zip(
              *[self.prompt_fn() for _ in range(batch_size)]
          )
  
          prompt_ids = self.sd_pipeline.tokenizer(
              prompts,
              return_tensors="pt",
              padding="max_length",
              truncation=True,
              max_length=self.sd_pipeline.tokenizer.model_max_length,
          ).input_ids.to(self.accelerator.device)
          prompt_embeds = self.sd_pipeline.text_encoder(prompt_ids)[0]
  
          with self.autocast():
              sd_output = self.sd_pipeline(
                  prompt_embeds=prompt_embeds,
                  negative_prompt_embeds=sample_neg_prompt_embeds,
                  num_inference_steps=self.config.sample_num_steps,
                  guidance_scale=self.config.sample_guidance_scale,
                  eta=self.config.sample_eta,
                  output_type="pt",
              )
  
              images = sd_output.images
              latents = sd_output.latents
              log_probs = sd_output.log_probs
  
          latents = torch.stack(
              latents, dim=1
          )  # (batch_size, num_steps + 1, ...)
          log_probs = torch.stack(
              log_probs, dim=1
          )  # (batch_size, num_steps, 1)
          timesteps = self.sd_pipeline.scheduler.timesteps.repeat(
              batch_size, 1
          )  # (batch_size, num_steps)
  
          samples.append(
              {
                  "prompt_ids": prompt_ids,
                  "prompt_embeds": prompt_embeds,
                  "timesteps": timesteps,
                  "latents": latents[
                      :, :-1
                  ],  # each entry is the latent before timestep t
                  "next_latents": latents[
                      :, 1:
                  ],  # each entry is the latent after timestep t
                  "log_probs": log_probs,
                  "negative_prompt_embeds": sample_neg_prompt_embeds,
              }
          )
          prompt_image_pairs.append([images, prompts, prompt_metadata])
  
      return samples, prompt_image_pairs
  
imports: import os
  import warnings
  from collections import defaultdict
  from concurrent import futures
  from typing import Any, Callable, Optional, Tuple
  from warnings import warn
  import torch
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from huggingface_hub import whoami
  from ..models import DDPOStableDiffusionPipeline
  from .None import BaseTrainer, DDPOConfig
  from .utils import PerPromptStatTracker
label_desc: Use image node_id 9 for calling the DDPOTrainer obj's underlying member method code with example usage: obj._generate_samples(iterations, batch_size) and returns: samples,  prompt_image_pairs